Drug discovery is a complex and resource-intensive process, often requiring the evaluation of thousands of compounds to identify a viable therapeutic candidate. Traditional methods are increasingly being supplemented by computational approaches to enhance efficiency and reduce costs. This paper presents a multi-objective optimization strategy using genetic algorithms (GAs) to streamline the drug discovery pipeline. By addressing key objectives such as efficacy, toxicity, and synthesis cost, our approach demonstrates significant potential in improving hit identification and lead optimization stages. The results indicate that GAs can effectively balance competing objectives, leading to the identification of promising drug candidates with optimal profiles.